Pattern mining is a key subfield of data mining that aims at developing algorithms to discover
interesting patterns in databases. The discovered patterns can be used to help …

Nonoverlapping sequential pattern mining (SPM) is a data analysis task, which aims at
identifying repetitive sequential patterns with gap constraint in a set of discrete sequences …

High average utility pattern mining is the concept proposed to complement drawbacks of
high utility pattern mining by considering lengths of patterns along with the utilities of the …

Gap constraint sequential pattern mining (SPM), as a kind of repetitive SPM, can avoid
mining too many useless patterns. However, this method is difficult for users to set a suitable …

Data mining has been actively studied, and it has become more important due to the
development of information technology and the demands of diverse applications, such as …

Closed high utility itemsets (CHUIs) and maximal high utility itemsets (MaxHUIs) are two
important concise representations of HUIs. Discovering these itemsets is important because …

Nonoverlapping sequential pattern mining (SPM) is a type of SPM with gap constraints that
can mine valuable information in sequences. One of the disadvantages of nonoverlapping …

Repetitive sequential pattern mining (SPM) with gap constraints is a data analysis task that
consists of identifying patterns (subsequences) appearing many times in a discrete …

Mining high utility itemsets are the basic task in the area of frequent itemset mining (FIM) that
has various applications in diverse domains, including market basket analysis, web mining …

The discovery of frequent closed high utility itemsets (FCHUIs) and frequent generators of
high utility itemsets (FGHUIs) is significant because they serve as important and concise …